TY - GEN
T1 - Efficient motor babbling using variance predictions from a recurrent neural network
AU - Takahashi, Kuniyuki
AU - Suzuki, Kanata
AU - Ogata, Tetsuya
AU - Tjandra, Hadi
AU - Sugano, Shigeki
N1 - Funding Information:
The work has been supported by the Program for Leading Graduate Schools, ?Graduate Program for Embodiment Informatics? of the Ministry of Education, Culture, Sports, Science, and Technology; JSPS Grant-in-Aid for Scientific Research (S)(2522005); ?Fundamental Study for Intelligent Machine to Coexist with Nature? Research Institute for Science and Engineering, Waseda University; MEXT Grant-in-Aid for Scientific Research (A) 15H01710; and Scientific Research on Innovative Areas ?Constructive Developmental Science? 24119003.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - We propose an exploratory form of motor babbling that uses variance predictions from a recurrent neural network as a method to acquire the body dynamics of a robot with flexible joints. In conventional research methods, it is difficult to construct real robots because of the large number of motor babbling motions required. In motor babbling, different motions may be easy or difficult to predict. The variance is large in difficult-to-predict motions, whereas the variance is small in easy-topredict motions. We use a Stochastic Continuous Timescale Recurrent Neural Network to predict the accuracy and variance of motions. Using the proposed method, a robot can explore motions based on variance. To evaluate the proposed method, experiments were conducted in which the robot learns crank turning and door opening/closing tasks after exploring its body dynamics. The results show that the proposed method is capable of efficient motion generation for any given motion tasks.
AB - We propose an exploratory form of motor babbling that uses variance predictions from a recurrent neural network as a method to acquire the body dynamics of a robot with flexible joints. In conventional research methods, it is difficult to construct real robots because of the large number of motor babbling motions required. In motor babbling, different motions may be easy or difficult to predict. The variance is large in difficult-to-predict motions, whereas the variance is small in easy-topredict motions. We use a Stochastic Continuous Timescale Recurrent Neural Network to predict the accuracy and variance of motions. Using the proposed method, a robot can explore motions based on variance. To evaluate the proposed method, experiments were conducted in which the robot learns crank turning and door opening/closing tasks after exploring its body dynamics. The results show that the proposed method is capable of efficient motion generation for any given motion tasks.
KW - Flexible joint robot
KW - Motor babbling
KW - Recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=84952053276&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-26555-1_4
DO - 10.1007/978-3-319-26555-1_4
M3 - Conference contribution
AN - SCOPUS:84952053276
SN - 9783319265544
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 26
EP - 33
BT - Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
A2 - Huang, Tingwen
A2 - Liu, Qingshan
A2 - Lai, Weng Kin
A2 - Arik, Sabri
PB - Springer Verlag
T2 - 22nd International Conference on Neural Information Processing, ICONIP 2015
Y2 - 9 November 2015 through 12 November 2015
ER -